Abstract
t—Differential evolution algorithm in solving complex function optimization problems, the problems of convergence rate and precision is not high. At the same time, there is a big difference in the performance of evolutionary algorithms for solving the different types of optimization problems. To solve above two problems, this paper proposes a dynamic multimodal differential evolution algorithm. Firstly, the dynamically population is used to improve the exploration ability of algorithms; In addition, the algorithm uses Four different types of mutation operator to Produce among individuals, choose the best among individuals to enter the next iteration , improved the algorithms's performance of solving different types of optimization problems. Through a variety of BenchMark functions to the algorithm simulation experiment, and comparing and several other classical differential evolution algorithm, show that this algorithm has better optimization performance. Keywords—Differential evolution, Convergence rate, Mutation Operator 一种动态多模式差分演化算法 董小刚 ,谢清 ,柯林 2 1) 九江学院信息科学与技术学院,九江 江西 中国
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